SlideCheck: 通过数据集分布引导病理基础模型的自监督预训练
SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
AI总结：
提出SlideCheck工具，利用冻结病理基础模型的特征，通过双头MLP评分异常和恶性证据，引导自监督预训练数据筛选，实验表明数据分布影响模型下游性能。
摘要：
病理基础模型在大量WSI衍生补丁流上进行预训练，而数据构建过程中的监督通常是切片级别、稀疏或异质的。这种不匹配使得理解和控制哪些生物模式进入预训练数据变得困难。我们提出SlideCheck，一个轻量级的预训练数据引导工具，建立在冻结的病理基础模型补丁特征之上。SlideCheck并非作为独立的补丁诊断模型，而是提供明确的异常和恶性评分，用于组织、过滤和审计病理预训练数据。SlideCheck使用双头MLP分别建模广泛的异常形态和恶性证据。正则化的特征空间评分器为补丁级证据估计提供监督锚点，而评分-注意力一致性将补丁评分与WSI级别的MIL注意力结合，挖掘高置信度伪标签。然后使用相同的评分构建广泛阳性ViT预训练子集，其中如果异常或恶性证据超过阈值，则选择补丁。实验表明，SlideCheck定义的数据分布影响自监督ViT预训练的下游行为，表明生物组成是病理基础模型开发中的重要可控因素。精心策划的子集可以接近全数据性能，表明明确评分的补丁池可能支持更高效和可审计的预训练数据构建。这些发现将SlideCheck定位为数据引导和审计层，用于将大型未分化补丁池转化为可控和可重用的预训练数据集。
英文摘要：
Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous. This mismatch makes it difficult to understand and control which biological patterns enter the pretraining data. We propose SlideCheck, a lightweight pretraining data guidance tool built on frozen pathology foundation model patch features. Rather than serving as a standalone patch diagnostic model, SlideCheck provides explicit abnormality and malignancy scores for organizing, filtering, and auditing pathology pretraining data. SlideCheck uses a dual-head MLP to separately model broad abnormal morphology and malignant evidence. A regularized feature-space scorer provides a supervised anchor for patch-level evidence estimation, while score-attention agreement combines patch scores with WSI-level MIL attention to mine high-confidence pseudo labels. The same scores are then used to construct broad-positive ViT pretraining subsets, where a patch is selected if either abnormality or malignancy evidence exceeds a threshold. Experiments show that SlideCheck-defined data distributions influence the downstream behavior of self-supervised ViT pretraining, indicating that biological composition is an important controllable factor in pathology foundation model development. Curated subsets can approach full-data performance, suggesting that explicitly scored patch pools may support more efficient and auditable pretraining data construction. These findings position SlideCheck as a data guidance and auditing layer for transforming large, undifferentiated patch pools into controllable and reusable pretraining datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
